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46 results for “non-communicable disease”
Database of non-communicable disease reports contributing to UN high-level meeting process (2000-2020)
<p>Database of the key non-communicable disease reports, policy papers, strategies and journal series published 2000-2020 that fed into the UN high-level meeting process</p>
Assessment of non-communicable diseases screening practices among university lecturers in Ghana – a cross sectional single centre study
<p>This section highlights the various methods used for this study. It covered study setting, study design, study approach, study population, sampling techniques, sample size calculation, inclusion and exclusion criteria, ethical consideration, data collection, data management and data analysis<strong>. </strong></p> <p> </p> <p><strong>Study Setting</strong></p> <p>The study was carried out at Kwame Nkrumah University of Science and Technology (KNUST), Kumasi between February to August, 2022. The study covered all the six (6) Colleges in the University.</p> <p> </p> <p><strong>Study Design</strong></p> <p>This was a cross sectional study to ascertain health check practices among university lecturers.</p> <p> </p> <p><strong>Study Approach</strong></p> <p>The study employed quantitative approach in which data was collected using questionnaires with both closed- and open-ended questions.</p> <p><strong>Study Population</strong></p> <p>The study population involved 838 Lecturers across the six Colleges at Kwame Nkrumah University of Science and Technology (KNUST), Kumasi. A study of the lecturer population per college revealed that Colleges of Health Sciences (highest) and Agric /Natural resources (lowest) were the outliers (Quality Assurance and Planning Office, 2020).</p> <p> </p> <p><strong>Sampling Technique </strong></p> <p> </p> <p>Simple probability technique was used to select the name of a college and the day/date to visit. Two sets of papers were folded with names of colleges (set 1) and day/date of visit (set 2). A picker picked one folded paper from each set and the name of the college and the day/date to visit was matched. In this case, the ordering of date and visit gave 1<sup>st</sup> College of Humanities & Social Sciences, 2<sup>nd</sup> College of Agric and Natural Resources, 3<sup>rd</sup> College of Art & Built Environment, 4<sup>th</sup> College of Engineering, 5<sup>th</sup> College of Science and 6<sup>th</sup> College of Health Sciences. We then used the ‘walk in’’ system to select the study participants. Within the days to visit a college, any lecturer we meet in his/ her office was a potential study participant.</p> <p> </p> <p> </p> <p> </p> <p><strong>Sample Size Calculation</strong></p> <p>The sample size was obtained using Yamane, 1967 formulae as shown below:</p> <p> </p> <p> </p> <p>Where n= is the population of Lecturers in at KNUST</p> <p>E= is the level of precision</p> <p>Therefore: n= 838</p> <p> 1+838 (0.0025)</p> <p>n = 838</p> <p>1+ 2.098</p> <p> </p> <p>838</p> <p>3.095</p> <p> </p> <p> n=270 </p> <p>However, due to logistical constrains, 205 participants were contacted across the 6 Colleges at Kwame Nkrumah University of Science and Technology. We then applied simple proportions to get the number of lecturers to be consulted in each college.</p> <p> </p> <p><strong>Inclusion and Exclusions Criteria</strong></p> <p>Inclusion criteria was made up of all Lecturers on KNUST campus who are in active service and consented to participate. All other staff not within this category were excluded from this research.</p> <p> </p> <p><strong>Ethical Considerations</strong></p> <p>Ethical approval was sought from the CHRPE, KNUST with approval reference no: CHRPE/AP/581/21. The aim of the research was explained to participants. Those who consented to participate in the research were given consent forms to sign and date. Again, participants were assured of confidentiality. Participants were told that, they were free to withdraw from the study in the cause of time. In other words, study participants were not coerced into the study.</p> <p> </p> <p><strong>Data Collection Tool</strong></p> <p>Data was collected using structured questionnaires. The questionnaires covered dietary intake, alcohol intake, issues on physical inactivity and tobacco use. Aside these four main risk factors of NCDs, the questionnaire also captured frequency of blood pressure checks, blood pressure outcome anytime it is checked (systolic and diastolic), frequency of general body check-up, frequency of anthropometric measurement checks (weight and height), an assessment of impressions about the outcome of weight and height checks, an assessment of intended measures to be taken depending on the outcomes of weight and health checked. Again, the general observation of the nature of job as a lecturer and health status especially the outcome of blood pressure monitoring were also assessed. The questionnaire also captured the socio-demographic status of Lecturers,</p> <p> </p> <p><strong>Data Management</strong></p> <p>Only the Research Team had access to data. Data was kept confidential. The researchers had planned of disposing data from the storage 5 years after the publication of this research. Collected data was entered and cleaned using Microsoft Excel spread sheet, and then imported into STATA version 14.0 (Stata Corp LP, College Station, Texas, USA) for statistical analysis and results.</p> <p> </p> <p><strong>Data Analysis</strong></p> <p>Descriptive statistics were used to summarize the characteristics of the study population by employing frequencies and percentages for categorical data. In addition, the degree of relatedness (association) was evaluated using Chi-square (χ<sup>2</sup>) or Fisher’s exact tests where appropriate with a p ≤0.05 assumed to be statistically significant. Both bivariate and multivariate logistic regression analyses were performed and adjusted for colleges effect to identify associations among the variables of interest. Variables having significant association in the logistic regression models were set at p≤0.05 with 95% confidence interval (95% CI) for both unadjusted and adjusted odds ratios (OR, AOR).</p> <p> </p> <p><strong>Variables</strong></p> <p>BP was selected as the dependent variable, and in turn define as Normal: ≤ 120/80 mmHg; Elevated: Systolic between 120-129 and diastolic ≤ 80; Hypertension: Systolic ≥ 130 or diastolic ≥ 80. Then dichotomized into Normal blood pressure: ≤ 120/80 mmHg and high blood pressure (Hypertension): ≥ 130/90 mmHg for logistic regression analyses. Independent variables were socio-demographics; gender, age, marital status, staff rank and lecturer’s colleges (categorized into binary variable; COHS /COS/COE and CABE/CANR/COHSS), family history of NCDs and health check status. In this study, the variable “very often” denotes (doing the activity in question more than 4 times a month), “often” denotes (doing the activity in question at least twice a month), and “not often” denotes (doing the activity in question once a month).</p> <p> </p> <p> </p>
Assessment of non-communicable diseases screening practices among university lecturers in Ghana – a cross sectional single centre study
<p><strong>Data Collection Tool</strong></p> <p>Data were collected using structured questionnaires. The questionnaires covered dietary intake, alcohol intake, issues with physical inactivity, and tobacco use. Aside from these four main risk factors of NCDs, the questionnaire also captured the frequency of blood pressure checks, blood pressure outcome anytime it is checked (systolic and diastolic), frequency of general body check-ups, frequency of anthropometric measurement checks (weight and height), an assessment of impressions about the outcome of weight and height checks, an assessment of intended measures to be taken depending on the outcomes of weight and health checked. Again, the general observation of the nature of the job as a lecturer and health status especially the outcome of blood pressure monitoring were also assessed. The questionnaire also captured the socio-demographic status of Lecturers,</p> <p> </p> <p><strong>Data Management</strong></p> <p>Only the Research Team had access to data. Data was kept confidential. The researchers had planned of disposing data from the storage 5 years after the publication of this research. Collected data was entered and cleaned using Microsoft Excel spread sheet, and then imported into STATA version 14.0 (Stata Corp LP, College Station, Texas, USA) for statistical analysis and results.</p> <p> </p> <p><strong>Data Analysis</strong></p> <p>Descriptive statistics were used to summarize the characteristics of the study population by employing frequencies and percentages for categorical data. In addition, the degree of relatedness (association) was evaluated using Chi-square (χ<sup>2</sup>) or Fisher’s exact tests where appropriate with a p ≤0.05 assumed to be statistically significant. Both bivariate and multivariate logistic regression analyses were performed and adjusted for colleges' effect to identify associations among the variables of interest. Variables having significant association in the logistic regression models were set at p≤0.05 with 95% confidence interval (95% CI) for both unadjusted and adjusted odds ratios (OR, AOR).</p> <p> </p> <p><strong>Variables</strong></p> <p>BP was selected as the dependent variable, and in turn define as Normal: ≤ 120/80 mmHg; Elevated: Systolic between 120-129 and diastolic ≤ 80; Hypertension: Systolic ≥ 130 or diastolic ≥ 80. Then dichotomized into Normal blood pressure: ≤ 120/80 mmHg and high blood pressure (Hypertension): ≥ 130/90 mmHg for logistic regression analyses. Independent variables were socio-demographics; gender, age, marital status, staff rank, and lecturer’s colleges (categorized into binary variables; Colleges, family history of NCDs, and health check status. In this study, the variable “very often” denotes (doing the activity in question more than 4 times a month), “often” denotes (doing the activity in question at least twice a month), and “not often” denotes (doing the activity in question once a month).</p> <p> </p>
Questionnaire: Co-occurrence of behavioural risk factors for non-communicable diseases among 40-year and above aged community members in three regions of Myanmar
<p>This questionnaire was applied as data collection tool for the dataset of "Co-occurrence of behavioural risk factors for non-communicable diseases among 40-year and above aged community members in three regions of Myanmar".</p>
Risk factors for non-communicable diseases in Bangladesh: Findings of the population-based cross-sectional national survey 2018
<p><span><strong><span>Objectives:</span></strong> To determine the national prevalence of risk factors of non-communicable diseases (NCD) in the adult population of Bangladesh. </span></p> <p><span><strong><span>Design: The study was a </span></strong>population-based national cross-sectional study.</span></p> <p><span><strong><span>Setting:</span></strong> This study used 496 primary sampling units (PSUs) developed by the Bangladesh Bureau of Statistics. The PSUs were equally allocated to each division and urban and rural stratum within each division.</span></p> <p><span><strong><span>Participants:</span></strong> The participants were adults aged 18-69 years, who were usual residents of the households for at least six months, and stayed the night before the survey. Out of 9900 participants, 8185 (82.7%) completed STEP-1 and STEP-2, and 7208 took part in STEP-3. </span></p> <p><span><b>Primary and secondary outcome:</b> The prevalence of behavioral, physical, and biochemical risk factors of NCD. Data were weighted to generate national estimates.</span></p> <p><strong>Results: </strong>Tobacco use was significantly (p<0.05) higher in the rural (45.2%) than the urban (38.8%) population. Inadequate fruit/vegetable intake was significantly (P<0.05) higher in the urban (92.1%) than in the rural (88.9%) population. The mean salt intake per day was higher in the rural (9.0 gm) than urban (8.9 gm) population. Among all, 3.0% had no, 70.9% had 1-2, and 26.2% had ≥3 NCD risk factors. The urban population was more likely to have insufficient physical activity (AOR: 1.2, 95% CI: 1.2–1.2), obesity (AOR: 1.5, 95% CI: 1.5–1.5), hypertension (AOR: 1.3, 95% CI: 1.3–1.3), diabetes (AOR: 1.6, 95% CI: 1.6–1.6), and hyperglycemia (AOR: 1.1, 95% CI: 1.1–1.1).</p> <p><strong>Conclusions: </strong>Considering the high prevalence of the behavioral, physical, and biochemical risk factors, diverse population and high-risk group targeted interventions are essential to combat the rising burden of NCDs. </p>
Impact of Community Health Workers on Adherence to Therapy for Non-Communicable Chronic Disease in Chiapas, Mexico
ClinicalTrials.gov study NCT02549495. IPD Sharing: NO. Countries: 1. Publications: 2.
Risk factors for non-communicable diseases in Bangladesh: Findings of the population-based cross-sectional national survey 2018
Open the record for dataset details and reuse information.
KENFIN-EDURA: Explaining non-communicable disease-related behaviour in the context of urbanization, family and wealth
<p><span>Background </span></p> <p><span>The prevalence of non-communicable diseases is increasing in lower-middle-income countries as these countries transition to unhealthy lifestyles. The transition is mostly predominant in urban areas. We assessed the association between wealth and obesity in two sub-counties </span><span>in Nairobi City County, Kenya, in the context of family and poverty.</span></p> <p><span>Results </span></p> <p><span>A total of 149 households, response rate of 93%, participated, 72 from Embakasi and 77 from Langata. Most of the participants residing in Embakasi belonged to the lower income and education groups whereas participants residing in Langata belonged to the higher income and education groups. </span><span>About 30% of the pre-adolescent participants in Langata were with at least overweight, whereas the respective number in Embakasi was only 6% (p<0.001). In contrast, the prevalence of adults (mostly mothers) with overweight and obesity was high (65%) and similar in the two study areas. Wealth</span> <span>(</span><span>b</span><span> = 0.01; SE 0.0; p=0.003) and income (</span><span>b</span><span> = 0.29; SE 0.11; p=0.009) predicted higher BMI z-score in pre-adolescents. </span></p> <p><span>Conclusions</span></p> <p><span>In Nairobi, pre-adolescent overweight was already highly prevalent in the middle-income area, while the proportion of women with overweight/obesity was high also in the low-income area. These results suggest that a lifestyle promoting obesity is prevalent even in lower income areas in urban Kenya, and this is a strong justification for promoting healthy lifestyles across all socio-economic classes.</span></p>
Dataset for: Factors associated with self-reported diagnosed asthma in urban and rural Malawi: observations from a population-based study of 3 non-communicable diseases
<p>This dataset was used in analyses reported on the in paper with the same title, published in PLOS Global Health.</p>
Data from: Identifying patterns of non-communicable diseases in developed eastern coastal China: a longitudinal study of electronic health records from 12 public hospitals
Objective: Few studies have examined the spectrum and trends of non-communicable diseases (NCDs) in inpatients in eastern coastal China, which is transforming from an industrial economy to a service-oriented economy and is the most economically developed region in the country. This study aimed to dynamically elucidate the spectrum and characteristics of severe NCDs in eastern coastal China by analysing patients' longitudinal electronic health records (EHRs). Setting: To monitor the spectrum of NCDs dynamically, we extracted the EHR data from 12 general tertiary hospitals in eastern coastal China from 2003 to 2014. The rankings of and trends in the proportions of different NCDs presented by inpatients in different gender and age groups were calculated and analysed. Participants: We obtained a total sample of 1,907,484 inpatients with NCDs from 2003 to 2014, 50.05% of whom were male and 81.53% were aged 50 years or older. Results: There was an increase in the number of total NCD inpatients in eastern coastal China from 2003 to 2014. However, the proportion of chronic respiratory diseases and cancer inpatients decreased over the 12-year period. Compared with men, women displayed a significant increase in the proportion of mental and behavioural disorders (P<0.001) over time. Additionally, digestive diseases and sensory organ diseases significantly decreased among men, but not women. The older group accounted for a larger and growing proportion of the NCD inpatients, and the most common conditions in this group were cerebral infarctions, coronary heart disease and hypertension. In addition, the proportion of 21- to 50-year-old inpatients with diabetes, blood diseases or endocrine diseases skyrocketed from 2003 to 2014 (P<0.001). Conclusions: The burden of inpatients' NCDs increased rapidly, particularly among women and younger people. The NCD spectrum observed in eastern coastal China is a good source of evidence for developing prevention guides for regions experiencing transition.
Supporting data for: Risk factors common to leading eye health conditions and major non-communicable diseases: A rapid review and commentary
<p><strong><span>Background</span></strong><span>: </span><span>To gain an understanding of the intersection of risk factors between the most prevalent eye health conditions that are associated with vision impairment and non-communicable diseases (NCDs).</span></p> <p><span><strong>Methods</strong>:</span><span> A</span><span> series of rapid reviews of reviews reporting on non-modifiable risk factors, age and sex, and modifiable risk factors, including social determinants, were conducted for five common eye health conditions that are the leading causes of vision impairment globally (refractive error including uncorrected refractive error, cataract, age-related macular degeneration (AMD), glaucoma, and diabetic retinopathy) and five prevalent NCDs (cancer, cardiovascular disease, chronic respiratory disease, dementia, and depressive disorders). </span></p> <p><span><strong>Results</strong>: </span><span>Eye health conditions and NCDs share many risk factors. Increased age was found to be the most common shared risk factor, associated with increased risks of AMD, cataract, diabetic retinopathy, glaucoma, refractive error, cancer, cardiovascular disease, chronic obstructive pulmonary disease, and dementia. Other shared risk factors included smoking, obesity, alcohol consumption (mixed results), and physical activity was protective, though limited evidence was found for eye conditions. Social determinants are well documented as risk factors for NCDs.</span></p> <p><span><strong>Conclusion</strong>:</span><span> There is substantial overlap in common established risk factors for the most frequent vision-impairing eye conditions and leading NCDs. Increasing efforts should be made to integrate preventative and risk reduction interventions to improve health, with the greatest shared benefits for initiatives that aim to reduce smoking, improve diet, and promote physical activity.</span></p>
Co-occurrence of behavioural risk factors for non-communicable diseases among 40-year and above aged community members in three regions of Myanmar
<p>Data were collected from 660 community members in three Regions of Myanmar in 2019.</p>
Nutritional Transition in the Maghreb and Prevention of Obesity and Non-communicable Diseases
ClinicalTrials.gov study NCT01844349. IPD Sharing: Not stated. Countries: 2. Publications: 5.
Enhancing Non-communicable Disease Prevention and Control Through Parent-Teachers-Development Agents Network in Ethiopia
ClinicalTrials.gov study NCT06639412. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Ugandan Non-Communicable Diseases and Aging Cohort
ClinicalTrials.gov study NCT02445079. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Mauritius and Rodrigues Non-Communicable Disease (NCD) Study
ClinicalTrials.gov study NCT07048717. IPD Sharing: YES. Countries: 1. Publications: 1.
A Personalized Prevention Program (PPP) Based on the Comprehensive Geriatric Assessment (CGA) for the Prevention of Multidimensional Frailty Related to Non-communicable Chronic Diseases (NCDs) in Olde
ClinicalTrials.gov study NCT06224556. IPD Sharing: NO. Countries: 1. Publications: 2.
School-based e-Health Non-Communicable Disease (NCD) Prevention Program
ClinicalTrials.gov study NCT06674798. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Foetal Exposure and Epidemiological Transition: Role of Anaemia in Early Life for Non-communicable Diseases Later
ClinicalTrials.gov study NCT02191683. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Guatemala Internal Medicine Physicians' Knowledge of Non-communicable Disease Clinical Preventive Services
ClinicalTrials.gov study NCT01515111. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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International Brain Laboratory public data
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OpenNeuro
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